Research on network traffic anomaly detection technology based on XGBoost

Xuanbin Lei; Jianwen Liu; Xiaoying Ye

doi:10.1117/12.3051635

18 November 2024 Research on network traffic anomaly detection technology based on XGBoost

Xuanbin Lei, Jianwen Liu, Xiaoying Ye

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340328 (2024) https://doi.org/10.1117/12.3051635
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

With the rapid development of Internet technology, network traffic anomaly detection has become an indispensable part of network security, which is of great significance for preventing network attacks and maintaining data security. This article focuses on the in-depth research of XGBoost based network traffic anomaly detection technology, aiming to improve the accuracy and efficiency of network traffic anomaly detection through an efficient gradient boosting decision tree algorithm called XGBoost under the integrated learning framework. XGBoost stands out in multiple machine learning competitions with its outstanding performance. Its advantages in processing high-dimensional sparse data, providing parallel computing power, and model interpretability make it an ideal candidate for solving network traffic anomaly detection problems. In the experimental section, we selected real network traffic datasets for verification and demonstrated the superiority of XGBoost model in anomaly detection tasks through comparative experiments, especially in handling nonlinear relationships and capturing complex patterns. In addition, through in-depth analysis of the importance of features, key factors affecting abnormal network traffic were revealed, providing a basis for the formulation of security strategies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuanbin Lei, Jianwen Liu, and Xiaoying Ye "Research on network traffic anomaly detection technology based on XGBoost", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340328 (18 November 2024); https://doi.org/10.1117/12.3051635

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